Probabilistic Forecasting Task
(Redirected from Distributional Forecasting Task)
		
		
		
		Jump to navigation
		Jump to search
		A Probabilistic Forecasting Task is a forecasting task that generates probability distributions over future outcomes.
- AKA: Probability Prediction Task, Distributional Forecasting Task, Uncertainty Quantification Task, Probabilistic Prediction Task.
 - Context:
- It can typically produce probabilistic forecasting task outputs as probability estimates.
 - It can typically require probabilistic forecasting task calibration for reliable predictions.
 - It can typically employ probabilistic forecasting task methods like bayesian inference.
 - It can often incorporate probabilistic forecasting task uncertainty in prediction intervals.
 - It can often evaluate probabilistic forecasting task performance using proper scoring rules.
 - It can often support probabilistic forecasting task decisions under uncertainty conditions.
 - It can range from being a Binary Probabilistic Forecasting Task to being a Multi-Category Probabilistic Forecasting Task, depending on its outcome space.
 - It can range from being a Point Probabilistic Forecasting Task to being a Density Probabilistic Forecasting Task, depending on its output type.
 - It can range from being a Short-Horizon Probabilistic Forecasting Task to being a Long-Horizon Probabilistic Forecasting Task, depending on its prediction timeframe.
 - It can range from being a Single-Variable Probabilistic Forecasting Task to being a Multi-Variable Probabilistic Forecasting Task, depending on its target dimension.
 - ...
 
 - Examples:
- Event Probabilistic Forecasting Tasks, such as:
 - Continuous Probabilistic Forecasting Tasks, such as:
 - Time Series Probabilistic Forecasting Tasks, such as:
 - ...
 
 - Counter-Examples:
- Point Forecasting Task, which produces single values.
 - Classification Task, which assigns discrete categorys.
 - Deterministic Prediction Task, which lacks uncertainty quantification.
 
 - See: Forecasting Task, Probability Distribution, Uncertainty Quantification, Proper Scoring Rule, Forecast Calibration, Bayesian Inference, Prediction Interval, Ensemble Forecasting, Statistical Prediction.